A4 Hazard Analysis
Group member: Huanghe YaoJing, Minxue Gu, Jinpu Cao
My task mainly focused on the analysis of Vulnerability of Vehicles against Flooding and visualizing the distribution of the number of vehicles per residential building
In this assignment, our group developed a hazard risk analysis, specifically, estimated the vehicle damages caused by flooding, for the Menlo Park City (with 25 CBGs) with a residential population in the Bay Area that is exposed to coastal flooding. The following mapping shows the boundary of the city and the layout of block groups as well as the residential buildings’ footprints (10472).
Flood Hazard and Exposure
Flood hazard can be described by Sea Level Rise (SLR) and storm frequency - ‘return period’ (RP). Different combinations of SLR and RP can represent floods of different degrees. In this report, we focus on 9 hazard scenarios which is the combinations of 3 SLR (0cm, 25cm, 50cm) and 3 RP (annual, 20-year, 100 year). The exposure situation can be visualized by overlaying the buildings footprints mapping on the top of flooding mapping.
The following mapping shows the exposure mapping in the most serious flood hazard situation - 100-year storm and 50 cm sea level rise. From the mapping we can see that a large amount of areas in the city are submerged by the flood. However, there are not too many residential buildings (only 7% ≈ 750/10472) submerged as we expected at the beginning. This might be one reason why most residential buildings are rather away from the coast. Similarly, for each scenario, we can get the corresponding exposure data. Combind all the results as our final exposure data.
Estimate Vehicle per Building
We use EMFAC to collect vehicle counts in the San Mateo county, for the years 2020, 2030, 2040, and 2050. Use this as an estimate of the % increase in vehicles decade by decade. Then collect the latest available ACS 5-yr data about vehicle ownership in the specific CBGs and produce an estimate of the total number of owned vehicles in Menlo Park City. We estimate the number of vehicles in 2030, 2040 and 2050. Here, we assume that the % vehicle ownership rate does not change over the next 30 years.
The total population in each block is calculated based on the 2020 Decennial census data. All building footprints within these blocks are retrieved from the OpenStreetMap data. Here we have not found the complete parcel data, thus, we just assume all buildings are residential buildings. Next, we assume population is distributed evenly across buildings in a block, and vehicles are distributed evenly across population. According to the assumption, vehicles in 2020 can be allocated from the whole CBG to each building. That is to say, \[vehicle\ per\ person = vehicle\ count/sum(pop) \] \[population\ per\ bldg = population\ /\ bldg\ count \] \[vehicle\ per\ bldg = vehicle\ per\ person\ *\ population\ per\ bldg\] Assume that vehicles stored in or near those buildings at ground level are subject to the same flood exposure. In general, the assumption makes sense because the building footprints does not change very much over the study period. Calculate average depth for each building under each of the hazard scenarios.
Vulnerability of Vehicles against Flooding
TO estimate the Vehicles damage caused by flooding, vulnerability of vehicles against flooding needs to be known first. In the page of “Economic Guidance Memoranda”, EGM 09-04 provides tables of vehicles depth-damage and content depth-damage for different vehicle types. From the following figure we can see that in general,Sports are the most vulnerable vehicles and SUVs are the most resistant vehicles in the face of flood.
PERCENT DAMAGE TO VEHICLES
So, we consider these two boundary cases. The following chart shows the vehicle damage during 100-year storm, by base sea level rise. The red line represents the most vulnerable case (all vehicles are Sports); The blue line represents the most resistant cese (all vehicles are SUVs). The real case should fall within this range.
Risk Estimation - Average Annual Loss
In order to measure the loss concretely and intuitively, we choose to convert the “loss percentage” of each vehicle into dollar loss. According to the U.S. News and the World Report study. the average cost of owning a car is $14,571. Besides, we assume that 20.57% of the vehicles are immune to the hazard because they are in operation. The data is extracted from the Experian Automotive’s AutoCount Vehicles in Operation database. Finally, we assume that 25% of the vehicles would be moved away from the hazard with the advanced warning. Based on the assumptions above, we can calculate the vehicle damages (in dollar) in the following equation. In the equation, we use the mean of the percent damage in the most vulnerable case and the percent damage in the most resistant case as the average percent damage.
\[ Vehicle\ damage\ = (1−percent\ move) \times (1−percent\ immune) \times cost\ per\ vehicle\times percent \ damage\] \[ Vehicle\ damage\ = (1−25\%) \times (1−20.57\%) \times 14,571 \times average\ percent \ damage\] The sea level rises in the next 30 years is random variables. Some studies proposed prediction models about the distribution. Here we use the distribution provided by RCP45. Given one SLR and RP, calculate one damage. Sum all possible situations (interpolation and integral) and we can get the average annual loss for each building under the influence of hazard.
From the mapping we can see that there are only 3 buildings suffering more than $1,000 loss per year (only for vehicle damage) in 2020. However, there will about 18 buildings suffering more than $1,000 loss per year (only for vehicle damage) in 2050, which means the sea level rise will affect our life obviously.
We aggregate the average annual loss data by block groups and get the following mapping. From the mapping we can see that the block group in the north suffers a lot in the next 30 years because of the flood hazard.
Finally, we want to explore the distribution of the number of vehicles per residential building. It is important since when the flood hazard coming, it would be very inconvenient if the number of vehicles per building is less than 1. From the following mapping we can see that there are some buildings (in red) facing this kind of bad situation. These residential buildings should be paid more attention to since they would suffer from the flood hazard and they do not have enough vehicles to get rid of it.